Komparasi Hasil Color Feature Extraction HSV, LAB dan YCrCb pda Algoritma SVM untuk Klasifikasi Spesies Burung

Sarwati Rahayu (Universitas Mercu Buana)
Andi Nugroho (Universitas Mercu Buana)
Erwin Dwika Putra (Universitas Muhammadiyah Bengkulu)
Mariana Purba (Universitas Sjakhyakirti)
Hadiguna Setiawan (Towwar Tech Ind)
Sulis Sandiwarno (Universitas Mercu Buana)

JSAI (Journal Scientific and Applied Informatics)
Vol 6 No 3 (2023): November

The classification of bird species is a problem often faced by ornithologists, and has been considered scientific research since antiquity. This study aims to evaluate the results of color feature extraction including HSV, LAB and YCrCb against the results of the SVM classification. In addition, the results of this study are useful to determine the performance of color feature extraction that is suitable for bird species classification. The dataset used was 22,617 bird species images. Based on experimental results, the effect of HSV on the SVM classification caused a decrease in accuracy by -0.33% while LAB and YCrCb on the SVM classification caused an increase in accuracy of 0.44% and 0.21%. However, the accuracy of the SVM classification does not yet have good performance so that further research will be carried out using other classifications, including convolutional neural networks and others.